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Update Warmup

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This commit is contained in:
D-X-Y
2020-10-08 10:19:34 +11:00
parent ad5d6e28b9
commit ab801cbf14
7 changed files with 90 additions and 43 deletions
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12
lib/models/shape_searchs/generic_size_tiny_cell_model.py
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@@ -2,8 +2,8 @@
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 #
#####################################################
# Here, we utilized three techniques to search for the number of channels:
# - feature interpaltion from "Network Pruning via Transformable Architecture Search, NeurIPS 2019"
# - masking + GumbelSoftmax from "FBNetV2: Differentiable Neural Architecture Search for Spatial and Channel Dimensions, CVPR 2020"
# - channel-wise interpaltion from "Network Pruning via Transformable Architecture Search, NeurIPS 2019"
# - masking + Gumbel-Softmax from "FBNetV2: Differentiable Neural Architecture Search for Spatial and Channel Dimensions, CVPR 2020"
# - masking + sampling from "Can Weight Sharing Outperform Random Architecture Search? An Investigation With TuNAS, CVPR 2020"
from typing import List, Text, Any
import random, torch
@@ -55,10 +55,10 @@ class GenericNAS301Model(nn.Module):
assert algo in ['fbv2', 'tunas', 'tas'], 'invalid algo : {:}'.format(algo)
self._algo = algo
self._arch_parameters = nn.Parameter(1e-3*torch.randn(self._max_num_Cs, len(self._candidate_Cs)))
if algo == 'fbv2' or algo == 'tunas':
self.register_buffer('_masks', torch.zeros(len(self._candidate_Cs), max(self._candidate_Cs)))
for i in range(len(self._candidate_Cs)):
self._masks.data[i, :self._candidate_Cs[i]] = 1
# if algo == 'fbv2' or algo == 'tunas':
self.register_buffer('_masks', torch.zeros(len(self._candidate_Cs), max(self._candidate_Cs)))
for i in range(len(self._candidate_Cs)):
self._masks.data[i, :self._candidate_Cs[i]] = 1
@property
def tau(self):